In [1]}, the authors study a model of MFGs that allows for costs and state dynamics to depend also on the distribution of actions of other players. Under somewhat stringent assumptions of Lipschitz continuity, an existence and uniqueness of equilibrium result is given, and an algorithm is presented for learning such an equilibrium. Of note, this paper assumes that the learner has access to a population simulator for sampling state transition and cost data at one's convenience. This algorithm is therefore not suitable for learning applications in which the data observed by a learner depends on an actual trajectory of play, obtained during sequential interaction with a system.
